We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to …
One of the major challenges facing researchers nowadays in applying deep learning (DL) models to Medical Image Analysis is the limited amount of annotated data. Collecting such ground-truth annotations requires domain knowledge (expertise), cost, and time, making it infeasible for large-scale databases. We presented a novel concept for training DL models from noisy annotations collected through crowdsourcing platforms, i.e., Amazon Mechanical Turk, Crowdflower, by introducing a robust aggregation layer to the convolutional neural networks. Our proposed method was validated on a publicly available database on Breast Cancer Histology Images showing interesting results of our robust aggregation method compared to baseline methods, i.e., Majority Voting. In follow-up work, we introduced a novel concept of an image to game-object translation in biomedical Imaging allowing medical images to be represented as star-shaped objects that can be easily embedded to readily available game canvas. The proposed method reduces the necessity of domain knowledge for annotations. Exciting and promising results were reported compared to the conventional crowdsourcing platforms.
A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classification problems is today a bottleneck for many applications. Recently, …
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical …
As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The …
Scene coordinate regression has become an essential part of current camera re-localization methods. Different versions, such as regression forests and deep learning methods, have been successfully applied to estimate the corresponding camera pose …